Barriers and facilitators for using administrative data for surveillance purpose: A narrative overview

Although administrative data are not originally intended for surveillance purposes, they are frequently used for monitoring public health and patient safety. This article provides a narrative overview of the barriers and facilitators for the use of administrative data for surveillance, with a focus on healthcare-associated infection (HAI) in Canada. In this case, only articles on administrative data in general or related to HAI were included. Validation study and meta-analyses on administrative data accuracy were excluded. Medline, Embase and Google Scholar were searched as well as references list of all included articles, for a total of 90 articles included. Our analysis identifies 78 barriers at the individual, organizational and systemic levels and outlines 75 facilitators and solutions to improve administrative data utilization and quality. This narrative overview will help to understand barriers, facilitators and offer practical recommendations for optimizing the use of administrative data.

Remote Diagnosis on Upper Respiratory Tract Infections Based on a Neural Network with Few Symptom Words-A Feasibility Study

This study aims explore the feasibility of using neural network (NNs) and deep learning to diagnose three common respiratory diseases with few symptom words. These three diseases are nasopharyngitis, upper respiratory infection, and bronchitis/bronchiolitis. Through natural language processing, the symptom word vectors are encoded by GPT-2 and classified by the last linear layer of the NN. The experimental results are promising, showing that this model achieves a high performance in predicting all three diseases. They revealed 90% accuracy, which suggests the implications of the developed model, highlighting its potential use in assisting patients' understanding of their conditions via a remote diagnosis. Unlike previous studies that have focused on extracting various categories of information from medical records, this study directly extracts sequential features from unstructured text data, reducing the effort required for data pre-processing.

Communication in Sudden-Onset Major Incidents: Patterns and Challenges-Scoping Review

OBJECTIVE

To identify and describe patterns and challenges in communication in sudden-onset major incidents.

METHODS

Systematic scoping review according to Joanna Briggs Institute and PRISMA-ScR guidelines. Data sources included Cochrane Library, EMBASE, PubMed/MEDLINE, Scopus, SweMed+, Web of Science, and Google Scholar. Non-indexed literature was searched as well. The included literature went through data extraction and quality appraisal as per pre-registered protocol.

RESULTS

The scoping review comprised 32 papers from different sources. Communication breakdown was reported in 25 (78.1%) of the included papers. Inter-authority communication challenges were reported in 18 (56.3%) of the papers. System overload and incompatibility was described in 9 papers (28.1%). Study design was clearly described in 30 papers (93.8%).

CONCLUSIONS

The pattern in major incident communication is reflected by frequent breakdowns with potential and actual consequences for patient survival and outcome. The challenges in communication are predominantly inter-authority communication, system overload and incompatibility, and insufficient pre-incident planning and guidelines.

Interpretation and Misinterpretation of Medical Abbreviations Found in Patient Medical Records: A Cross-Sectional Survey

Introduction  Medical abbreviations are used in patient medical records across all departments within the hospital setting and upon discharge. Abbreviations can have more than one contradictory or ambiguous definition, which can result in errors in communication due to misunderstanding or misinterpretation. Modern patient care is multidisciplinary, so there should be no room for ambiguity in patient medical records. Therefore, the aim of this survey was to assess individual interpretations and misinterpretations of a list of medical abbreviations found in patient medical records, and thereby increase awareness of the growing use of non-standard abbreviations.  Materials and methods  In this cross-sectional survey, anonymized questionnaires containing a list of 20 abbreviations were given to a convenience sample of consultant physicians, doctors-in-training, and nurses, all of whom are involved in the day-to-day use of patient medical records. Volunteers were asked to define each abbreviation in full. A provided definition was either the intended definition (given a score of one) or completely different in terms of text and meaning (alternative definition). The intended definitions, alternative definitions, and number of abbreviations that were defined by at least 50% of volunteers were collated. Abbreviations that had more than 50% of volunteers providing the intended definition, were regarded as "generally accepted" abbreviations. Volunteers were assured that this was not a test of knowledge and that questionnaires were completely anonymized.  Results  In total, 46 volunteers completed questionnaires. Volunteers consisted of 15 nurses, 15 doctors-in-training, and 16 consultant physicians. The number of volunteers who provided the intended definition for each abbreviation ranged from zero to 87%, depending on the abbreviation. Only four out of 20 abbreviations (20%) had more than 50% of volunteers providing the intended definition and thus regarded as "generally accepted". The maximum score achieved among the volunteers was 12 out of 20 (60%), and the minimum score achieved was 2 out of 20 (10%). The overall mean score achieved by the volunteers was 6.39 out of 20 (32%). Only one-quarter of the volunteers achieved a score above 50%. Additionally, 75% of the abbreviations had one or more (one to seven) alternative definitions.  Conclusions  This survey demonstrated that non-standard medical abbreviations used in patient medical records were being misunderstood or misinterpreted. A majority of abbreviations were not recognized among user groups. Additionally, three-quarters of abbreviations had one or more alternative definitions. Healthcare institutions should encourage the reporting of errors arising from the usage of abbreviations, and introduce initiatives to discourage the use of non-standard abbreviations in patient medical records.

Disambiguating Clinical Abbreviations Using a One-Fits-All Classifier Based on Deep Learning Techniques

Background  Abbreviations are considered an essential part of the clinical narrative; they are used not only to save time and space but also to hide serious or incurable illnesses. Misreckoning interpretation of the clinical abbreviations could affect different aspects concerning patients themselves or other services like clinical support systems. There is no consensus in the scientific community to create new abbreviations, making it difficult to understand them. Disambiguate clinical abbreviations aim to predict the exact meaning of the abbreviation based on context, a crucial step in understanding clinical notes.

Objectives  Disambiguating clinical abbreviations is an essential task in information extraction from medical texts. Deep contextualized representations models showed promising results in most word sense disambiguation tasks. In this work, we propose a one-fits-all classifier to disambiguate clinical abbreviations with deep contextualized representation from pretrained language models like Bidirectional Encoder Representation from Transformers (BERT).

Methods  A set of experiments with different pretrained clinical BERT models were performed to investigate fine-tuning methods on the disambiguation of clinical abbreviations. One-fits-all classifiers were used to improve disambiguating rare clinical abbreviations.

Results  One-fits-all classifiers with deep contextualized representations from Bioclinical, BlueBERT, and MS_BERT pretrained models improved the accuracy using the University of Minnesota data set. The model achieved 98.99, 98.75, and 99.13%, respectively. All the models outperform the state-of-the-art in the previous work of around 98.39%, with the best accuracy using the MS_BERT model.

Conclusion  Deep contextualized representations via fine-tuning of pretrained language modeling proved its sufficiency on disambiguating clinical abbreviations; it could be robust for rare and unseen abbreviations and has the advantage of avoiding building a separate classifier for each abbreviation. Transfer learning can improve the development of practical abbreviation disambiguation systems.

Structure, content, unsafe abbreviations, and completeness of discharge summaries: A retrospective analysis in a University Hospital in Austria

RATIONALE AND OBJECTIVE

The discharge summary (DS) is one of the most important instruments to transmit information to the treating general physician (GP). The objective of this study was to analyse important components of DS, structural characteristics as well as medical and general abbreviations.

METHOD

One hundred randomly selected DS from five different clinics were evaluated by five independent reviewers regarding content, structure, abbreviations and conformity to the Austrian Electronic Health Records (ELGA) using a structured case report form. Abbreviations of all 100 DS were extracted. All items were scored on a 4-point Likert-type scale ranging from "strongly agree" to "strongly disagree" (or "not relevant"). Subsequently, the results were discussed among reviewers to achieve a consensus decision.

RESULTS

The mandatory fields, reason for admission and diagnosis at discharge were present in 80% and 98% of DS. The last medication was fully scored in 48% and the recommended medication in 94% of 100 DS. There were significant overall differences among clinics for nine mandatory items. In total, 750 unexplained abbreviations were found in 100 DS.

CONCLUSIONS

In conclusion, DS are often lacking important items. Particularly important are a detailed medication history and recommendations for further medication that should always be listed in each DS. It is thus necessary to design and implement changes that improve the completeness of DS. An important quality improvement can be achieved by avoiding the use of ambiguous abbreviations.

Danger in discharge summaries: Abbreviations create confusion for both author and recipient

BACKGROUND

The transition from hospital inpatient care to medical care in the community is a high-risk period for adverse events. Inadequate communication, including low quality or unavailable discharge summaries, has been shown to impact patient care.

AIMS

Assess use of abbreviations in clinical handover documents from inpatient hospital teams to general practitioners (GPs), and the interpretation of these abbreviations by GPs and hospital-based junior doctors.

METHODS

Retrospective audit of 802 discharge summaries completed during a one-week period in 2017 by a Queensland regional health service. GPs and local junior doctors then attempted interpretation of twenty relevant abbreviations.

RESULTS

99% (794) discharge summaries included abbreviations. 1612 different abbreviations were used on 16 327 occasions. The median number of abbreviations per discharge summary was 17 (range 0-86). 254 GPs and 62 junior doctors responded to a survey which found that no abbreviation was interpreted the same by all respondents. GPs and junior doctors were unable to offer any interpretation in 17.9% and 15.2% of cases respectively. GPs offered a greater range of interpretations than junior doctors, with a median of 9 and 3 different interpretations per abbreviation respectively. 94% (239) of GPs felt that the use of abbreviations in discharge summaries had the potential to impact patient care. 152 (60%) GPs felt that time spent clarifying abbreviations in discharge summaries could be excessive.

CONCLUSIONS

Abbreviations are often used in discharge summaries, yet poorly understood. This has the potential to impact patient care in the transition period after hospitalisation This article is protected by copyright. All rights reserved.